Method and system for automatically targeting ads to television media using demographic similarity

ABSTRACT

Described herein is a system and method of ad targeting that automatically matches advertisements to media based on the demographic signatures of each. The method and system include calculating a match score between historical buyer demographics and media demographics. Media which is similar to the demographic of the product buyers is targeted for advertising.

CROSS-REFERENCES

This application claims the benefit of U.S. patent application Ser. No.13/209,346, entitled “METHOD AND SYSTEM FOR AUTOMATICALLY TARGETING ADSTO TELEVISION MEDIA USING DEMOGRAPHIC SIMILARITY”, filed Aug. 12, 2011,and U.S. Provisional Application No. 61/372,974, entitled “METHOD ANDSYSTEM FOR AUTOMATICALLY TARGETING ADS TO TELEVISION MEDIA USINGDEMOGRAPHIC SIMILARITY”, filed Aug. 12, 2010, and U.S. ProvisionalApplication No. 61/378,299, entitled “SYSTEM AND METHOD FOR ATTRIBUTINGMULTI-CHANNEL CONVERSION EVENTS AND SUBSEQUENT ACTIVITY TO MULTI-CHANNELMEDIA SOURCES”, filed Aug. 30, 2010, all of which are herebyincorporated by reference in their entirety.

BACKGROUND

Brief definitions of several terms used herein follow, which may behelpful to certain readers. Such definitions, although brief, will helpthose skilled in the relevant art to more fully appreciate aspects ofthe invention based on the detailed description provided herein. Suchdefinitions are further defined by the description of the invention as awhole and not simply by such definitions.

Asset A specific impression, airing, or advertising events. For example,a instance media asset may be CNN-Monday through Friday. An Assetinstance of this asset may be CNN-Tuesday-8:05pm-AC360. Media assetAdvertising media that can be published for purposes of advertising.Examples include television airing, radio spot, newspaper spot, internetpublisher page. For example, a television media asset may comprise somecombination of station-geography-program-day-hours such asWXGN-Florida-SixO'Clock.News-Monday-6pm, or may be a more general setsuch as FOX-Monday through Friday. Asset Same as media asset. Media Sameas media asset Station Some as media asset Station-Program Same as mediaasset Station-Program- Some as media asset Day-Hour Product Somethingthat is being sold by an advertiser. The product and advertisement areused interchangeably—each product is assumed to have one or moreadvertisements that can be aired on television media. The contents ofthe advertisement are considered part of the product for the presentsystem in order to simplify the description. Advertisement Same asProduct

A variety of methods have been used in the past for televisionadvertising targeting.

Television Ad Targeting Using Intuition

The first method which is widely used today uses human intuition andinsight to identify which television programs to purchase. This methodcan be effective, however, doesn't scale. There are over 2 millionpossible television advertising placements during a year even withconservative estimates of airing frequencies, and many more when localbroadcast schedules are considered (there are 5,000 local stations), andso picking the right times, geographies, and programs to run isdifficult.

Television Ad Targeting Using the Nielsen Television Panel

A second method for targeting television advertisements is to useNielsen age-gender ratings. Nielsen's viewer panel was developed in the1960s and consists of 5,000 to 25,000 households spread around theUnited States. The viewing panels themselves range from fully electronicrecording systems to paper-based diaries.

Nielsen panelists track their viewing habits, and then Nielsenaggregates the data and shows the age-gender demographics for eachprogram. A media buyer can then decide whether to purchase advertisementfor this programming if it seems to be like the audience that would wantto purchase the product.

Television Ad Targeting Using Historical Ad-Media Performance

Another form of television ad targeting uses historical data fromprevious advertisement airings on media, and their performance, in orderto predict whether buying another airing with the sameprogram-station-day-hour might be effective. This technique is mostcommonly used for “Television long-form buying”, also known as“Infomercials”. In order to determine if “DIY Saturday midnight-1 am”will perform well, the ad company looks for how the ad performed in thissame time-slot and station a week prior (for example). The best exampleof this method that we know of besides our own work in this area is fromTellis et. al. (2005) which presents an automated system of this kind.The system includes lag-terms for ad placements, and responses collectedover the past several hours. Tellis, G., Chandy, R., Macinnis, D.,Thalvanich, P. (2005), “Modeling the Microeffects of TelevisionAdvertising: Which Ad Works, When, Where, for How Long, and Why?”,Marketing Science, Marketing Science 24(3), pp. 351-366, INFORMS.

Television Ad Targeting Using One-to-One Techniques

Television Ad Targeting should theoretically be able to be performed byanalyzing an individual television viewer's activity to determine whatproducts they're interested in, and delivering an advertisement to thatspecific person.

At the moment this technology is not available for television except insome very limited—generally research-related—cases. Most of the work inthis area comprises experiments, tests in the New York area where someone-to-one capabilities have been installed via the cable system in thatarea, and academic studies. There are technological problems around bothtracking viewer activity, as well as delivering an ad to them withoutdelivering it to lots of other customers. The overwhelming majority oftelevisions as of 2011 have no capability for individualized tracking orad delivery.

One-to-one television ad targeting was first discussed as early as the1970s. Personalized television programming has since been proposed bySmyth and Cotter (2000) and Spangler et al., (2003). Smyth, B. andCotter, P. (2000), “A Personalized Television Listings Service”,Communications of the ACM, Vol. 43, No. 8, August 2000, pp. 107-111;Spangler, W., Gal-Or, M., May, J. (2003), “Using data mining to profileTV viewers”, Communications of the ACM archive, Volume 46, Issue 12(December 2003), pp. 66-72. One-to-one targeting in a TV context hasbeen described conceptually by (Arora, N., Hess, J., Joshi, Y., Neslin,S., Thomas, J. (2008), “Putting one-to-one marketing to work:Personalization, customization, and choice”, Marketing Letters, Vol. 19,pp. 305-321.). Chorianopoulos, Lekakos, Spinellis (2003) and Lekakos andGiaglis (2004) ran experiments which tested the effectiveness ofpersonalized advertising on television. Chorianopoulos, K. and G.Lekakos and D. Spinellis (2003). “The Virtual Channel Model forPersonalized Television”, Proceedings of the 1st EuroiTV conference:From Viewers to Actors pp. 59-67. They recruited experimental subjectsand had them fill out surveys to classify them into segments. They nextused a training set of users who had explicitly indicated their interestin some advertisements to predict interest in the new ads.

BRIEF DESCRIPTION OF THE DRAWINGS

A brief description of the drawings follows.

FIG. 1.1 shows sample media plan data. This is the data that is producedby media buyers purchasing media to run in the future. This includeswhat station the commercial will run on, what advertiser, what creative,what the media cost associated with the purchased data is, whatindividual phone numbers and web address will be associated to thecommercial for tracking purposes.

FIG. 1.2 is sample data that is generated by 3rd party verificationservices. These services watermark commercials and then monitor when themedia was run across all TV stations. This is analogous to a web pixelserver in online advertising. The purpose of this data feed is to verifythat the media that was purchased actually ran.

FIG. 1.3 is sample trafficking instructions/order confirmation sent tostations. This is confirmation of what was purchased and instructs thestations when to run which commercial creatives (advertiser's 30 secondmedia asset). This can also tell the station to a/b test across multipleversions of a creative, etc.

FIG. 1.4 shows sample call center data. This data records the phoneresponses to phone numbers displayed in the commercial creatives.Essentially each phone number is unique to each commercial or stationand time.

FIG. 1.5 shows sample e-commerce data. This data is for orders that cameon an advertiser's website. This data shows the customer information andtime the orders came in.

FIG. 1.6 shows the actual order data that is placed in the advertiser orretailers system. This data is final purchase record and is typicallyupdated with subsequent purchases for subscriptions, bad debt collectionproblems, and returns.

FIG. 1.7 shows a sample of consumer data enrichment. Typically this is awide array of hundreds of attributes about consumers from the variousdata bureaus. This includes demographics, psychographics, behavioralinformation (such as recent purchases), household information, etc.

FIG. 1.8 shows sample program guide data. This is the data of theprogramming that is going to be run in the future on individual stationsin terms of time, program name, program type, stars, and general textdescription.

FIG. 1.9 shows sample audience panel data. This data is from a rollup ofprevious purchasers and their associated demographics, psychographics,etc from FIG. 1.7. Alternatively, this could also come from set top boxdata appended with the same sets of attributes or other existing viewerpanels.

FIG. 1.10 is a flow diagram illustrating an example basic process ofadvertisement targeting.

FIG. 1.11 is a flow diagram illustrating an example enhanced process ofadvertisement targeting.

FIG. 1.12 shows a plot of orders per advertisement as a function ofsimilarity.

FIG. 1.13 shows a box plot of orders per advertisement airing byquartile of similarity score.

FIG. 2 shows an example screenshot of different segments that can betargeted.

FIG. 3 shows an example program finder result for media assets.

FIG. 4 shows an example allocation of an advertising budget to programstations.

FIG. 5 shows an example graph of targeted impressions and impressions asan increasing number of media assets are added to the advertisingpurchase.

FIG. 6 shows an example screenshot of a program finder result thatincludes a graph of targeted impressions and impressions for a givenbudget level and a sortable list of suggested programs.

FIG. 7 shows an example screenshot showing different media assetpatterns that can be compared to a product.

FIG. 8 shows an example screenshot of how customer records are input tothe program finder.

FIG. 9 shows an example screenshot of a program finder profile builder.

FIG. 10 shows an example result of top rotations determined by theprogram finder.

FIG. 11 shows an example program finder result for program media assetpatterns.

FIG. 12 shows an example screenshot of a program finder result fortargeting television media.

FIG. 13 shows an example screenshot of a program finder result fortargeting television media.

FIG. 14 shows an example screenshot of a program finder result fortargeting television media.

FIG. 15 shows a geographic distribution of product buyers.

FIG. 16 shows lift scores for customer demographic attributes forproduct buyers for a magazine product.

FIG. 17 shows the distribution for age for a particular magazineproduct, including the lift distributions for the natural clusters thatwere automatically generated by the system.

DETAILED DESCRIPTION

Aspects of the inventive system, as described herein, recognize that themedia is represented by the demographics of people who are watching.Using this insight, the system can create a “fingerprint” for the kindsof customer that buys a product of interest. The system can then performa vector match against media looking for the closest match. After thesystem finds a close match, it recommends buying that media. Althoughthe technology for one-to-one television advertisement targeting is notcurrently available, aspects of the current invention are designed towork with one-to-one targeting capabilities and can utilize one-to-onetracking and delivery when that becomes available. The present inventionwould simply add in the individualized information to its segmentinformation in order to improve the ad match quality.

The method used by a media buyer using Nielsen aggregated data todetermine which program to purchase is not based on any formal method(such as vector match), nor is it based on analysis of the product buyerpopulation. Furthermore, while the Nielsen panel is a useful data sourceand use of this data is described in this application, the Nielsenviewer panel is inherently limited by its very small size, minimalability to cover different geographic areas, and this has remained aconsistent problem with using Nielsen data for deciding whether topurchase ads for a particular programming. A variety of enhancements arediscussed for making the techniques described below compatible withmultiple other demographic sources (including census, Set top box data,and linked buyer data) so as to create a highly complete and richprofile based on millions of viewers, over 400 variables, not just 2 or20 as are available through the Nielsen panel, and buyers rather thanviewers.

Various examples of the invention will now be described. The followingdescription provides specific details for a thorough understanding andenabling description of these examples. One skilled in the relevant artwill understand, however, that the invention may be practiced withoutmany of these details. Likewise, one skilled in the relevant art willalso understand that the invention may include many other obviousfeatures not described in detail herein. Additionally, some well-knownstructures or functions may not be shown or described in detail below,so as to avoid unnecessarily obscuring the relevant description.

The terminology used below is to be interpreted in its broadestreasonable manner, even though it is being used in conjunction with adetailed description of certain specific examples of the invention.Indeed, certain terms may even be emphasized below; however, anyterminology intended to be interpreted in any restricted manner will beovertly and specifically defined as such in this Detailed Descriptionsection.

System Architecture

Prior to being able to do response modeling and detailed targeting fortelevision media, a large amount of system infrastructure should be inplace. The following discussion provides a brief, general description ofa suitable computing environment in which the invention can beimplemented. Although not required, aspects of the invention aredescribed in the general context of computer-executable instructions,such as routines executed by a general-purpose data processing device,e.g., a server computer, wireless device or personal computer. Thoseskilled in the relevant art will appreciate that aspects of theinvention can be practiced with other communications, data processing,or computer system configurations, including: Internet appliances,hand-held devices (including personal digital assistants (PDAs)),wearable computers, all manner of cellular or mobile phones (includingVoice over IP (VoiP) phones), dumb terminals, media players, gamingdevices, multi-processor systems, microprocessor-based or programmableconsumer electronics, set-top boxes, network PCs, mini-computers,mainframe computers, and the like. Indeed, the terms “computer,”“server,” and the like are generally used interchangeably herein, andrefer to any of the above devices and systems, as well as any dataprocessor.

Aspects of the invention can be embodied in a special purpose computeror data processor that is specifically programmed, configured, orconstructed to perform one or more of the computer-executableinstructions explained in detail herein. While aspects of the invention,such as certain functions, are described as being performed exclusivelyon a single device, the invention can also be practiced in distributedenvironments where functions or modules are shared among disparateprocessing devices, which are linked through a communications network,such as a Local Area Network (LAN), Wide Area Network (WAN), or theInternet. In a distributed computing environment, program modules may belocated in both local and remote memory storage devices.

Aspects of the invention may be stored or distributed on tangiblecomputer-readable media, including magnetically or optically readablecomputer discs, hard-wired or preprogrammed chips (e.g., EEPROMsemiconductor chips), nanotechnology memory, biological memory, or otherdata storage media. Alternatively, computer implemented instructions,data structures, screen displays, and other data under aspects of theinvention may be distributed over the Internet or over other networks(including wireless networks), on a propagated signal on a propagationmedium (e.g., an electromagnetic wave(s), a sound wave, etc.) over aperiod of time, or they may be provided on any analog or digital network(packet switched, circuit switched, or other scheme).

Step 1: Setup Data Feeds with Media Agency

A first step is to ensure all the data about what media is beingpurchased, running, and trafficked to stations is collected to ensurethat there is an accurate representation of the television media. Thisincludes setting up data feeds for

a. Media Plan Data (FIG. 1.1)

b. Media Verification Data (FIG. 1.2)

c. Trafficking/Distribution Data (FIG. 1.3)

Step 2: Setup Data Feed with Call Center

A second step is to ensure there is accurate data about the callers thatcalled into specific phone numbers from the call center and it isimportant to get the call center onboarded with a data feed (FIG. 1.4).

Step 3: Setup Data Ecommerce Vendor Datafeeds

A third step is to setup recurring data feeds with the vendor orinternal system of the advertiser that records orders that come in fromthe advertiser's website (FIG. 1.5)

Step 4: Step Data Order Processing/Fulfillment Data Feed

A fourth step is to setup recurring data feeds with the order vendor orinternal system that physically handles the logistics of billing and/orfulfillment. This is important for subsequent purchases such assubscriptions and for returns/bad debt, etc to accurately account forrevenue. This may also come from a series of retail Point of Sale system(FIG. 1.6).

Step 5: Setup Audience Data Enrichment Data Feed with Data Bureau

A fifth step is to ensure that every caller, web-converter, and ultimatepurchaser has their data attributes appended to their record in terms ofdemographics, psychographics, behavior, etc (FIG. 1.7). Examples of DataBureaus are Experian, Acxiom, Claritas, etc.

Step 6: Setup Data Feed with Guide Service

A sixth step is to ensure that the forward looking guide service data isingested into the system. This is the programming of what is going torun on television for the weeks ahead (FIG. 1.8). This upcoming mediawill be scored by the current invention to determine which of this mediashould be purchased.

Step 7: Setup Data Feed for Panel Data Enrichment

Either through the purchasers of products on television, set top boxviewer records, or existing panels it is necessary to get a feed ofviewer/responder data that has the same demographic, psychographic,behavioral data appended to that is being appended to the advertiser'spurchaser data in Step 5 (FIG. 1.9).

Step 8: Ingest all Data into Staging System

In step 8, all of the underlying data is put into production and all ofthe data feeds setup from Steps 1-7 are loaded into an intermediateformat for cleansing, adding identifiers, etc. Personally IdentifiableInformation (PII) is also split and routed to a separate pipeline forsecure storage.

Step 9: Run Business Logic I Models for Matching Responses and Orders toMedia

In step 9, all of the data from the data feeds has been ingested intothe system at the most granular form. Here the phone responses arematched up to the media that generated it. The a-commerce orders arematched using statistical models to the media that likely generatedthem.

Step 10: Load Data into Final Databases

In step 10, the data is aggregated and final validation of the resultsis automatically completed. After this, the data is loaded into thedatabases for use with any of the upstream media systems. These includethe ability to support media planning through purchase suggestions,revenue predictions, pricing suggestions, performance results, etc.

Step 11: Use Data in Presentation Layer

In step 11, all of the data becomes accessible to the operators ofvarious roles in the media lifecycle. This includes graphical tools formedia planning (where the targeting in this application primarily fits),optimization, billing, trafficking, reporting, etc.

System Inputs and Outputs

The user provides the following information:

A1 Inputs

-   -   1. Customer name and address, telephone number, cookie, or some        other record identifying customers    -   OR    -   2. a manual specification for one or more demographic profiles        that the user would like to target

B1. Outputs

-   -   1. List of Media Assets and their corresponding Fitness scores

A2. Optional Inputs

-   -   The user may also provide other information, although this is        not necessary for the invention to perform ad targeting:    -   3. One or more Segments for each customer record, to which this        customer belongs (optional)    -   4. Keywords related to the product (optional)    -   5. Maximum Cost or Budget: The amount of money that the user        would like to spend (optional)    -   6. Minimum ROI: The ratio of revenue/cost that the user would        like to maintain. Technically this is a constraint in which        media will be purchased subject to being greater than this        value, but it is often colloquially referred to as a “goal” or        “objective” (optional)    -   7. Minimum relevance: A score such as a tratio or correlation        coefficient (described later) indicating how well the television        programming matches the product demographics. The system may        identify cheap media to purchase, but the user may want to        maintain a minimum standard for relevance when performing ad        targeting.    -   8. Notification guidelines: Specifications for how the system        should contact the user to either seek review and confirmation        of purchasing the media assets, or whether the system has the        user's authority to purchase the media without further        notification (“auto-plot” version).

B2. Optional Outputs

-   -   2. Purchase instructions for Media Assets: The system will        identify media assets to purchase and then proceed to create a        “buy order” for this media.

Details of the Ad-Program Targeting Algorithm

The steps of the ad-program targeting algorithm are given below. Thesesteps are shown in a flow chart in FIGS. 1.10 and 1.11.

-   -   1. Create a product profile    -   2. Create a media profile    -   3. Calculate the demographic similarity between product and        media profiles    -   4. Use product-media demographic similarity as one of the        factors when buying television media    -   5. Add historical media performance information    -   6. Add contextual match information between product and station    -   7. Compute final prediction of media-product fitness based on        any of the above factors

Step 1: Create Product Profile

The first step is to identify which kinds of customers would like to buythe product. In many cases, an advertiser already has a significantnumber of customers who have bought the product previously. Thesecustomers are known because they have had to provide their credit cardnumber, name, address, and phone number as part of the order process.

The system can take this database of customers, and enrich the customerrecords with demographics. For example, using the zip-code of thecustomer it is possible to infer their household value using US Censusdata. Using name it is often possible to infer gender and ethnicity(e.g. “Christine”->Female, “Bob”->Male). A variety of third partyservices for customer demographics exist, and can be used for thispurpose, for example, Acxiom, which maintains an extensive database, andenriches to over 400 variables including income, age, gender, interests,and so on.

After enriching the customer records, the system can now create anaverage profile for customers who have bought this product.

Customer demographics can be defined as r_(i,Dj,k) where D_(j) is thejth demographic variable for customer response i and product k. Theproduct profile p_(j,k) will be defined as the average of the manycustomer demographics where that demographic is not a missing value, andthe customer in question purchased product P(i,k).

$p_{j,k} = {\frac{1}{I}{\sum\limits_{i:{P{({i,k})}}}r_{i,D_{j},k}}}$

After creating the product profile, the system can report on the mostdistinctive attributes of the product profile. Assume that there are alarge number of customers with demographic enrichment, from a variety ofproducts k. The system can calculate a grand mean or population centroidas follows:

$p = {\frac{1}{IK}{\sum\limits_{k}{\sum\limits_{i}r_{i,D_{j},k}}}}$$\sigma = \sqrt{\frac{1}{IK}{\sum\limits_{i,k}\left( {r_{i,D_{j},k} - p_{j,k}} \right)^{2}}}$

Each product profile element can be expressed as a z-score compared tothe population mean and standard deviation:

$z_{j,k} = \frac{\left( {p_{j,k} - p} \right)}{\sigma}$

The operation above ensures that each profile value is re-scaled so thatits difference from the mean is scaled to units of standard deviations.Therefore, all of the dimensions are now transformed into the samez-score scale. Higher zscores means more unusually high variablecompared to the population. While a z-score is used in the calculationshere, any standardized or normalized statistical score can be calculatedfor each variable and used in a similar manner.

Table I shows a product profile for a handyman tool product thatindicates that customers enjoy woodworking and auto-repair. They buyunusual amounts of “big and tall male apparel”, smoke at a higher ratethan the population, and engage in outdoor activities and even likefishing.

Table II shows a product profile for a cat product. The highest z-scoreis “cat owner”. The buyers are also older, are interested inenvironmental issues, and give to charities.

FIGS. 12-14 show some screenshots of the product profile for thehandyman product. The colored bars either show that the demographicvariable-value is higher than a reference population (green, extendrightwards) or lower than normal (red, extend leftwards). In this case,these graphs show that buyers of a handyman product are often male,republican, have an age range between 45-75, incomes ranging from75K-125K and so on.

One other embodiment is to display the product profile information in amanner called “key insights”. The Key insights section (the left handcolumn in FIG. 12) shows the variable-values that are unusually highcompared to the reference population (more on reference populationslater). In order to improve the presentation of this information, thevariable-value with the highest observed-percent/referencepopulation-percent should be displayed, but for each variable, only themaximum variable-value should be shown—the remainder should besuppressed to improve readability. For example, a particular profilemight have “Income=125K . . . 150K”=+50% and “Income=100K . . .124K”=+25%, and these may be the highest variable-values. The firstvariable-value should be shown, and the second should be suppressedsince the maximum from the variable has already been selected. This hasthe effect of picking the most distinctive variable-values from eachvariable category, which aids in readability since it is easier to seethe different variables that are distinctive for the customers.

FIG. 15 shows a geographic distribution of product buyers. Greenrepresents more buyers per capita. The per capita counts, along withcensus-based demographic match correlation, each provide for additionalmedia asset patterns that are used when calculating the fitness scorefor any potentially buyable media asset.

FIG. 16 shows lift scores for customer demographic attributes forproduct buyers for a magazine product. These lift scores are generatedautomatically, and using “key insights” the most distinctivedemographics are provided in a summary view.

FIG. 17 shows the distribution for age for a particular magazineproduct, including the lift distributions for the natural clusters thatwere automatically generated by the system. The system generates theseclusters and lift-scores automatically and provides them to the user.

Such insights into the buyer population could be used to optimize theadvertising program. For example, because the cat buyers are interestedin environmental issues, the advertising creative could be modified tomention that the cat product is bio-degradable. Since the buyers like togive to charities, the advertiser might offer to donate 5% of proceedsto a charity such as an animal shelter.

A. Product Clusters and Segments for Ad Targeting (Optional)

The above steps create a single product centroid. It is also possible tocreate multiple clusters for use in ad targeting. One embodiment is todo this using unsupervised clustering such as K-Means algorithm.

Another embodiment is to utilize segments that have been pre-defined bythe client, and then create a profile for each of those pre-definedsegments. Examples of segments can include, but are not limited to,recently acquired customers, such as within the last three months, thetop 10% of customers based upon dollar amount purchased, and the bottom10% of customers based upon dollar amount purchased.

TABLE I DEMOGRAPHIC PRODUCT PROFILE FOR PROJECT 10023 (HANDYMANPRODUCT). Variable z-score Woodworking 0.588898 AutoWork 0.491806Automotive, AutoPartsandAccessories-SC 0.48754 SportsandLeisure-SC0.470066 HomeImprovement-Do-It-Yourselfers 0.447953 Gardening 0.438412Camping/Hiking 0.43656 Gardening-C 0.429214 HomeImprovement 0.421209HomeandGarden 0.417073 SportsGrouping 0.407583 HomeImprovementGrouping0.39556 AgeinTwo-YearIncrements-InputIndividualID 0.392807 DIYLiving0.392767 Computers 0.387011 PCOwnerID 0.386974 OutdoorsGrouping 0.385564TruckOwner 0.369703 CreditCardHolder-UnknownType 0.369275 BankCardHolder0.36921 HomeFurnishings/Decorating 0.365209 Apparel-Men's-C 0.382816Income-EstimatedHouseholdID 0.360737 Crafts 0.357646Income-EstimatedHousehold-NarrowRangesID 0.349372AgeinTwo-YearIncrements-1stindividualID 0.342792 Fishing 0.342675

TABLE II DEMOGRAPHIC PRODUCT PROFILE FOR PROJECT 10019 (CAT PRODUCT).Variable z-score CatOwner 0.72978 OtherPetOwner 0.507782AgeinTwo-YearIncrements-InputIndividualID 0.497612AgeinTwo-YearIncrements-lstIndividualID 0.470661 Pets-SC 0.460489Community/Charities 0.382429 CollectiblesandAntiquesGrouping 0.382163Value-PricedGeneralMerchandise-SC 0.380277 Collectibles-General 0.378843Gardening 0.362677 EnvironmentalIssues 0.356035 Gardening-C 0.339688HomeImprovernentGrouping 0.339207 HomeLiving 0.334977 SportsGrouping0.328068 Cooking/FoodGrouping 0.325231 Movie/MusicGrouping 0.32243HomeFurnishings/Decorating 0.322362

Step 1.2 Alternative Method for Defining the Product Profile

An alternative method for defining the product profile is to ask a userto “manually define” the profile that they would want to target. Forexample, they could specify that they would like to target:

-   -   1. Persons who have an interest in fishing +50%    -   2. Persons who have more than 4 cars −100%    -   3. Persons who have more than 4 children +100%    -   4. Persons who have diabetes interest +50%    -   5. And so on

In one embodiment, the user specifies these variables by defining thepercentage above or below the “reference population” for thisdemographic. In the above example, the user specified interest inidentifying media for people who are 50% higher than the population interms of interest in diabetes.

Based on the above “manually defined” profile, these lift scores can nowbe translated back into product z-scores by setting:z=((diff+1)*mean)/stdev. The remainder of the matches to media thenperforms the same as described in the following sections.

Step 2: Create Media Profile

The next step is to create a demographic profile of television mediaassets. If the television media has customer viewership information itmay already have demographics associated with it. In this case the“linkage” between the person and the media is a definite viewing eventby that person.

In another embodiment, television media that is linked to buyers by wayof a unique phone number or other device to tie the media to the personcan be used. Linking keys can be any of a (a) telephone number, (b) URL,(c) offer, (d) cookie, or some other identifier in the advertisementthat is uniquely associated with an advertisement on the media. When thecustomer uses the key to buy the product, that customer can be tied backto the unique broadcast where they saw the embedded linking key.

The system can create an average profile for customers that have beenlinked to each media. For every media S_(i) S_(i,Dj) is defined as thejth demographic of the media S_(i). Each media S_(i) is equal to the sumof its constituent asset instances and the customers who were linked tothose spots. Thus each media demographic profile is an average of thecustomer demographic vector who purchased from asset instances linked tothe media. In one embodiment, the media demographic profile can be usedto predict the demographic viewership of media assets.

An example of this kind of media demographic profile is shown in TableIII. “Do It Yourself” (“DIY”) station watchers have interest inWoodworking, Hunting, Gardening, Sport and Leisure, tend to be male (Bigand Tall Male apparel). They also own dogs and smoke at a higher ratethan the rest of the population. Please note the significant similaritybetween DIY profile and that of the handyman tool product. This will beuses in the next step.

Table IV shows a television station profile for “Animal Planet”. Thetelevision audience for this station tends to be older, female, ownspets, and so on. There are a variety of traits also in common with thecat product.

TABLE III DEMOGRAPHIC PRODUCT BUYER PROFILE FOR DO IT YOURSELF CHANNELVariable z-score Gender-InputIndividualID −3.45 Reading- 3.21FinancialNewsletterSubscribers Apparel-Men's-BigandTall-C 2.38Donation/Contribution-C 1.97 DogOwner 1.62 SeniorAdultinHouseholdID 1.61Woodworking 1.57 Collectibles-Antiques 1.54 Hunting/Shooting 1.52Gardening-C 1.49 Golf 1.38 SportsandLeisure-SC 1.32 OutdoorsGrouping1.29 Arts 1.19 Smoking/Tobacco 1.18

TABLE IV DEMOGRAPHIC PRODUCT PROFILE FOR ANIMAL PLANET TV CHANNELVariable z-score Gender-InputIndividualID −3.08 Pets-SC 2.55 CatOwner2.35 Veteran 2.30 Donation/Contribution-C 2.25 Gardening- 2.23Collectibles-Antiques 2.22 OtherPetOwner 2.15 Woodworking 2.14Apparel-Children's-C 2.13 History/Military 2.09 Apparel-Women's-C 1.89PCOperatingSystemID 1.83 EnvironmentalIssues 1.81

In one embodiment, the media demographic profile can be reported as anindex showing the proportion of demographics on each media asset. Forexample, the average income level of viewers of a particular televisionstation is 50 percent above the average income level in the UnitedStates, and this can be indicated using an index.

In another embodiment, the media demographic profile can be reported asthe population equivalent numbers of viewers on each media asset. Forexample, the number of viewers of a particular television station thatis male and between the ages of 30 and 32 can be given as a percentageof the general population.

A. Calculate Media Profile Using Viewer Panel

An alternative method for calculating the media profile is to use aviewing panel demographics, and to summarize the profile based on theviewing panel. In one embodiment the viewing panel may make availabletheir demographics, and may make available the media that they arewatching throughout the day. As a result, it is now possible tosummarize the media profile as the average of demographics of the panelviewers (an example is the Nielsen panel which maintains 5,000-25,000customers and some demographics).

In another embodiment, the media profile is calculated using informationobtained from unique set-top boxes that have been assigned televisionviewers to record the television viewing history of the viewers andusing demographic information provided by the viewers (an example is Settop box data provided by a company such as Tivo and which hasdemographics appended via a company such as Acxiom).

As will be appreciated by those skilled in the art, there may be othertechniques to monitor viewers' television viewing history and obtaindemographic information from those viewers. A media profile can becalculated using these other techniques.

Step 4: Calculate Product-Television Station Similarity (Using LinkedBuyer Records)

The disparity δ between the product and television station program canbe calculated as below.

δ(p _(i,D) _(j) ·S _(iD) _(j) )=|p _(i,D) _(j) −S _(iD) _(j) |

A. Basic Normalization for Calculation of Similarity

In measuring the disparity between spot and customer responsedemographics, it is necessary to appropriately scale the variables tomaximize the effectiveness of the match. Demographic variables rangefrom ordinal values in the tens (e.g. age ranges from 18 . . . 80) to“has children” which is a two-value binary variable, 0,1. If thevariables aren't scaled then in an L1-distance calculation, the agevariable would tend to exert up around 50× more “weight” on the distancematch than gender. Yet gender may be just as valuable as age. Because ofthis, the system standardizes each disparity to z-scores. Thetransformation is

${\Delta \; \left( {p_{m,},S_{k}} \right)} = \frac{\sum_{j}{w_{j}{Z\left( {p_{m,D_{j}},S_{{kD}_{j}}} \right)}}}{\sum_{j}w_{j\;}}$${Z\left( {p_{m,D_{j}},S_{k,D_{j}}} \right)} = \frac{{\delta \left( {p_{i,D_{j}},S_{k,D_{j}}} \right)} - {\frac{1}{J}\Sigma_{i,k}{\delta \left( {p_{i,D_{j}},S_{k,D_{j}}} \right)}}}{\sqrt{\frac{1}{J}{\Sigma_{i,k}\begin{bmatrix}{{\delta \left( {p_{i,D_{j\;}},S_{k,D_{j}}} \right)} -} \\{\frac{1}{J}\Sigma_{i,k}{\delta \left( {p_{i,D_{j}},S_{k,D_{j}}} \right)}}\end{bmatrix}}^{2}}}$

Z is the standardized discrepancy for demographic j. wj is a weight ondemographic j which can be computed numerically by regression, orentered by a user to place more or less weight on the match betweendifferent demographics. D is the overall average weighted discrepancybetween the demographics of product and media.

Each demographic is compared against the distribution of its disparitiesto determine whether it is high or low compared to the norm fordisparity.

As a result, the system provides a similarity score, with the lowervalue indicating better similarity between the product and stationdemographics.

Table V shows the top list of stations that match the handyman product.ESPN, HGTV, DIY, HISTORY channel, Hallmark, which are all very similarstations.

TABLE V Closest Television Stations For Project 10023 (HandyMan ToolProduct) Project key Station natural key Similarity 10023 KL4 −0.30510023 ESPNU −0.302 10023 HGTV −0.300 10023 DIY −0.297 10023 DG8 −0.29610023 DISH_Network −0.294 10023 HALL −0.293 10023 HIST −0.292 10023ESPN2_Local −0.291 10023 CRT5 −0.288

B. Alternative Methods for Normalizing the Media Profile

In order to help reduce bias, the invention allows for the definition ofdifferent panels of persons as “reference populations” to create z-scoreparameters. For example, if customers were acquired through the web,then the appropriate means and standard deviations for these customerswould be based on a large population of web users (typically web usersare younger, higher income, etc). The product vector is then summarizedusing z-scores relative to the mean and standard deviation of thereference population. Similarly, customers acquired through televisionphone response should be compared to a large set of television phoneresponders; in general, these responders tend to be older. By comparingthe customers to the appropriate reference population, the influence ofthe acquisition vehicle can be reduced.

The invention allows multiple “reference populations” to be maintained,and used for comparison—one particular embodiment includes an ecommercepanel (customers who are active on the web), DRTV panel (persons whohave telephoned in response to a television ad), and 1% of US populationpanel (persons who are sampled to approximately 1% of US population).

Mapping Between Different Demographic Sources

In the above discussion, both customers who are linked to media, as wellas product buying customers, are each enriched with the same set ofdemographics (eg. both might have Acxiom data enrichment). However, insome instances, product customer demographics and media assetdemographics will originate from different sources. For example,customer records associated with linked buyers can be enriched with dataobtained from a third party that provides consumer demographics, e.g.Acxiom, while media asset viewer demographics can be obtained from aviewing data provider, e.g., Tivo. With different demographics sources,a transform or demographic conversion can be used to convert thecustomer demographics into comparable media asset demographics, or viceversa.

In one embodiment this is implemented by creating (a) an aggregationlayer which maps multiple source demographics and numerically transformsand then aggregates them based on parameters that that are set (eg.transform could just be a sum), and (b) a mapping table which maps thenewly created aggregated variable from source 1 demographic to source 2demographic (so that the appropriate “income>$120K” in source 1 maps toan appropriate “income>$120K” variable in source 2).

For example, in source 1 there may be the categories “age 18”, “age 19”,“age 20”, and these are aggregated to “age 18-20” using the mappinglayer, and then mapped to an equivalent “age 18-20” variable that existsin the second demographic source. Using this method, profile vectors canbe created using the aggregated/transformed demographic representation,and mapped one-to-one to media vectors in a second source that have alsobeen transformed in this way.

Tables showing example mapping of variables from one demographic toanother are shown in Table VI and Table VII. Table VI shows examples ofdemographic mapping from some Acxiom variables to some US Censusvariables (over 20 variables can be matched). The mapping between Acxiomvariables to US Census variables is used to match product vector with400 elements enriched by Acxiom data to US Census data.

Table VII shows an example mapping of some Acxiom variables to someNielsen variables (about 10 variables match). The mapping between Acxiomvariables to Nielsen variables is used to match Acxiom-enriched productdemographic vector against Nielsen Media assets that have a limitednumber of variables available.

Using this mapping technique, match scores between products andGeographic areas including Direct Marketing Association areas,Television Cable Zones, Zip codes, States, and “Advertising Patches” (aunit that Lowes stores use) and other Geographic areas) can becalculated, allowing upcoming television airings in local areas to betargeted in part based on customer characteristics in their geographicarea.

Also using this technique, match scores between the product and theNielsen panel can be created, allowing the TV media that the Nielsenpanelists watched to be appropriately scored, as another source ofinformation about the media. The same technique can also be used for SetTop Box data such as that provided by Tivo—in this case, income,ethnicity, age, gender, and other variables can be mapped in the sameway, allowing match scores to be created against Tivo-provided Media.These match scores can subsequently be combined to create an overallscore for value of targeting some media asset in question.

TABLE VI Examples of Demographic Mapping Between Some Acxiom Variablesand Some US Census Variables Acxiom Demographics Aggregator CensusAcxiom Demographics Name Value Age20to21 TotalPopFemale20AgeinTwo-YearIncrements- Age 20-21 InputIndividualDetail Age20to21TotalPopFemale21 AgeinTwo-YearIncrements- Age 20-21InputIndividualDetail Age20to21 TotalPopMale20 AgeinTwo-YearIncrements-Age 20-21 InputIndividualDetail Age20to21 TotalPopMale21AgeinTwo-YearIncrements- Age 20-21 InputIndividualDetail Age22to24TotalPopFemale22to24 AgeinTwo-YearIncrements- Age 22-23InputIndividualDetail Age22to24 TotalPopMale22to24AgeinTwo-YearIncrements- Age 22-23 InputIndividualDetail Age22to24TotalPopFemale22to24 AgeinTwo-YearIncrements- Age 24-25InputIndividualDetail Age22to24 TotalPopMale22to24AgeinTwo-YearIncrements- Age 24-25 InputIndividualDetail

TABLE VII Example Demographic Mapping Between Some Acxiom Variables andSome Nielsen Variables Enriched- Enriched- Nielsen- Nielsen DemographicsDemographics Demographics MappingID Variable Variable Variable 1africanamerican Black18-65 African American 2 male MaleAdult Adult Male3 female FemaleAdult Adult Female 4 workingwoman WorkingWomanAdultWorking Woman 5 child6to8 C6-8 Child 6-8 6 child9to11 C9-11 Child 9-11Generating Media Assets from the Guide Service

In the discussion above, the product vector was matched to the mediaasset vector. More details about the media asset will now be described.Media may be purchased in several forms—for example, a specificTelevision program such as “AC360”, a Rotator such as “CNN M-F 6 p-9 pm”or a broad station such as “CNN”, and Geographic-specific versions ofthe same such as “CNN Seattle”.

Using the upcoming media schedule, each of these “purchasable mediagrains” are applied to the schedule, and all feasible “purchasablemedia” are extracted and scored. These “purchasable media” are the mediaassets that are referred to throughout this disclosure.

Getting a Combined Media Asset Score from Multiple Media Asset Patterns

There's one further step that is required in order to produce apractical estimate of similarity. Each media asset itself is comprisedof several different “features” which carries information about theultimate performance of the particular media instance that is underconsideration. For example, let's say that on the upcoming schedule,there is the program “AC360” on CNN next Monday at 7 pm, for atelevision viewer located in Seattle. Predicting the performance of thisinstance should be possible using information about the performance of:the station “CNN”; eg. higher income people tend to watch; the program“AC360”; television on “Mondays at 7 pm on any station”; eg. fewer oldpeople percentage-wise watch during this time; television on “CNN onMondays at 7 pm”; television on “CNN Monday 7 pm in Seattle”; thegeographic area of “Seattle”; younger demographic exists in this area;the Rotation “CNN M-F 6 p-9 p”; the rotation “CNN 6 p-9 p”; and so on.Each of these features may convey information about the asset, as a“media asset pattern”. Examples of media asset patterns includedistributor (station), program, genre, distributor-rotation, day ofweek-hour of day, day of week, hour of day, media market, state, stateper capita, Direct Marketing Association customers per capita, Cablezone, and Cable zone customers per capita.

For each media asset, a similarity match is computed between the M mediaasset patterns that comprise the media asset, and the product. There areM similarity scores. The invention combines this evidence to create anoverall score for the fitness of the upcoming airing. An exampleembodiment is as follows:

d(p,s)=sum i(w(i)*d(p,mediaassetpattern(s,i))) where i e [1 . . . M]

An example of combining media asset patterns is provided below for thecase of trying to estimate the similarity of a rotator to the product.As discussed, a rotator comprises a wide variety of programming, times,and days, and as a result, the final estimate for similarity needs toblend all of these constituent components. For example, consider CNN“M-F 6 p-9 p”. Three programs each night might be covered by thisrotator. The vector match is calculated based on all of the persons inthat period and their demographics. Next, the similarity of “Program 1”,“Program 2”, “Program 3”, “CNN”, “CNN M-F”, and other media assetpatterns, are each combined with weights to provide a final estimate forthe similarity of this exact rotation instance which covers the aboveprogramming and patterns.

Tables VIII-XIV below show examples of different media asset patternsand correlation or similarity scores for an example product (handymanproduct):

TABLE VIII Program-level: Fox Nascar and similar programming shows ahigh correlation with the product purchasers of the handyman productlookup key Program corr P-1 FOX NASCAR SPRINT PRE 0.8358 P-2 NASCARSERIES AT DOVER 0.8357 P-3 FOX NASCAR SPRINT PRE SAT 0.8241 P-4 FOXNASCAR SPRINT SAT PRE 0.8103 P-5 FOX SPRINT CUP WNNRS CIRC 0.8036 P-6FOX NASCAR SPRNT SAT WNNR 0.7996 P-7 FOX NASCAR SPRNT WNNR SAT 0.7905P-8 MASH M-F 2 0.7901 P-9 FOX SPRINT CUP PACE LAP 0.7791 P-10 FOX NASCARSPRINT CUP PST 0.7787

TABLE IX Genre level: Documentary and news programs score well lookupGenre Corr Q-1 DOCUMENTARY, GENERAL 0.496117 Q-2 DOCUMENTARY, NEWS0.489461 Q-3 NEWS 0.461017 Q-4 OFFICIAL POLICE 0.443524

TABLE X Station level: Media asset patterns TWC, Fox News, and Historyinternational score well lookup Station corr S-1 TWC 0.683457 S-2 HI0.677207 S-3 FOXNC 0.613114 S-4 CNBC 0.603963 S-5 HLMC 0.584917 S-6 HIST0.563381 S-7 DIY 0.563147 S-8 IMG MEDIA 0.54394 S-9 BBCA 0.523207 S-10NGC 0.511578

TABLE XII Day of week: These persons are most heavily represented intelevision media during Sunday. Y lookup rownum weekday DOW corr 1 Y-18575 1 Sunday 0.298701477 2 Y-2 8576 3 Tuesday 0.270696067 3 Y-3 8577 2Monday 0.264775938 4 Y-4 8578 6 Friday 0.257218758 5 Y-5 8579 4Wednesday 0.256585593 6 Y-6 8580 5 Thursday 0.2562688 7 Y-7 8581 7Saturday 0.201279356

TABLE XII Day-Hour: 6 pm is a good time of day to target for thesecustomers, lookup hour corr day-hour a-1 1-18.0 0.45940398 Sunday-18.0a-2 3-18.0 0.4507812 Tuesday-18.0 a-3 7-18.0 0.44892917 Saturday-18.0a-4 5-18.0 0.43370198 Thursday-18.0 a-5 1-21.0 0.43338671 Sunday-21.0a-6 2-18.0 0.43201616 Monday-18.0 a-7 6-21.0 0.42660428 Friday-21.0 a-87-21.0 0.42047077 Saturday-21.0 a-9 4-18.0 0.41009863 Wednesday-18.0a-10 6-18.0 0.40573727 Friday-18.0

TABLE XIII Rotator: History international has rotators that match thesecustomers well. lookup hour corr zzz-1 HI-Sa-Su + 9 a-8 p 0.769536 zzz-2HI-M-Su + 8 p-12 a 0.760089 zzz-3 TWC-M-Su + 6 a-9 a 0.746814 zzz-4FOXNC-M-Su + 6 a-9 a 0.728315 zzz-5 HI-M-F + 6 p-8 p 0.714299 zzz-6HI-M-F + 3 p-6 p 0.708095 zzz-7 TWC-M-F + 3 p-6 p 0.702081 zzz-8TWC-Sa-Su + 9 a-8 p 0.685666 zzz-9 TWC-M-F + 6 p-8 p 0.683632 zzz-10HI-M-F + 9 a-3 p 0.667747

TABLE XIV Direct Marketing Association Area (Geographic area): Thesepersons are well-represented in rural areas. LOOKUP DMA corr zzzzzz-2BUTTE, MT 0.348392 zzzzzz-3 NORTH PLATTE, NE 0.325346 zzzzzz-4 MISSOULA,MT 0.316646 zzzzzz-5 TWIN FALLS, ID 0.316364 zzzzzz-6 Lincoln 0.316109zzzzzz-7 CHEYENNE, WY 0.314863 zzzzzz-8 SPOKANE, WA 0.313262 zzzzzz-9BURLINGTON, VT-PQ 0.309855 zzzzzz10 HONOLULU, HI 0.290508

Step 5: Calculate Product-Media Similarity (Using Viewer Panel)

Similar to the preceding discussion, a demographic similarity based onviewer panel can be defined by dv(p,s) where dv is the demographicsimilarity, p is the product and s is the media.

Step 6: Calculate Contextual Product-Television Station Match

An additional aspect of the current invention is that it is able toutilize keywords to improve the match between the product and mediaasset. This is done by defining a set of words related to theadvertising product, and also defining words related to the media. Thematch between these two sets of words is then calculated, and this formsan additional source of evidence regarding the quality of the media fortargeting the product.

This is done using the following steps:

-   -   1. Extract keywords from the media asset. This can be done        by (i) extracting text descriptions of programs from the        television schedule (eg. as available from a number of sources        such as Tribune Media), (ii) extracting text from closed        captioning of programs (closed captioning is a system whereby        spoken words in the program are transcribed into text which        appears on the screen. This can then be captured as part of the        broadcast and analyzed), (iii) extracting text from        speech-to-text recognition software applied against television        programs, (iv) extracting text from structured or        semi-structured program or schedule metadata, such as        contributors, locations, genres, characters, etc.    -   2. Calculate the term frequency-inverse document frequency        (tf-idf) score for each television media keyword. For each word,        set its tf-idf score to be        number-of-occurrences/expected-rate-of-occurrence-in-natural-corpus.        The expected rate of occurrence in a natural corpus is the rate        of occurrence of a word in a corpus such as public domain        literature or another source of text. Let this be the program        word bag vector.    -   3. Extract keywords for product. This can be done by analyzing        some text descriptions of or metadate about the product. For        example, a description of the product can be entered, a URL        which has information about the product can be provided, or the        user can manually type in distinctive keywords that describe the        product (eg. travel luggage might have keywords “traver”,        “luggage”, “bag”).    -   4. Calculate the term frequency-Inverse document frequency        (tf-idf) score for each of the product keywords, equal to        number-of-occurrences/expected-rate-of-occurrence-in-natural-corpus.        Let this be the ad bag of words vector.    -   5. Calculate the match score between the media asset and the        product. This can be done by calculating a function of the bag        of words vector of the program and the product.        -   a. A specific embodiment is be the correlation of log tf-idf            scores between the two word vectors.        -   b. Another specific embodiment may be to calculate the            binomial distribution probability of the two bag-of-words            vectors having the same keywords in common.

Step 7: Estimate Historical Product-Television Media Performance

When the product has previously been run on television media (eg.“CNN-AC360-Tues-8 pm”) and the performance of that media is available,this information can be incorporated into the prediction for the fitnessof the television media to the product. Historical product-televisionmedia performance is calculated as a function of the revenue and cost ofthe media from previous airings.

Historical product-television media performance data is extremelypredictive, since if an ad performed well on a specific program andday-time such as “AC360” it will likely perform well on the same programday-time again. However, it is generally the case that historical datais very minimal, since only a tiny fraction of TV media can be directlysampled in this way. In addition, the method for being able to trackrevenue due to the program is often limited and may include phonenumbers or vanity URLs attached to the advertisement, and so not all ofthe revenue associated with the ad may be tracked. As a result, whilethis is a useful technique, it is only part of the process for assessingthe quality for an upcoming airing.

One specific embodiment that creates a Historical product-mediaperformance estimate is as follows: Tables of historical data forStationPerformance(StationID,Cost,Revenue,Impressions,RevenuePerImpression,RevenuePerCost),StationDayPerformance(StationID,Day,Cost,Revenue,Impressions,RevenuePerImpression,RevenuePerCost), StationDayHourPerformance(StationID,Day,Hour,Cost,Revenue,Impressions,RevenuePerImpression,RevenuePerCost), andProgramPerformance(ProgramID,Cost,Revenue,Impressions,RevenuePerImpression,RevenueperCost)are created, where each time a particular media asset is used foradvertising, the results are recorded into the tables above. This issummarized to create average revenue per impression for each program,station, station-day, etc. For any new upcoming airing, a prediction ofperformance based on the historical performance of the ad in this samespot can be created as:

HistoricalRevenuePerImpressonPrediction=1*hitoricalStationRevenuePerimpression+a2*StatonProgramRevenuePerImpression+a3*StationDayRevenuePerImpression+a4*StationDayHourRevenuePerImpression+a5

The total impressions for any upcoming spot are estimated separately byusing Nielsen rated data or Set top box data using the same summarytable method (eg. StationDayHourImpressions, StationImpressions, etc),and then the performance for any upcoming spot is estimated as:

HistoricalRevenuePrediction=b1*ImpressionPredicton*HistoricalrevenuePerImpressionPrediction

TABLE VIII TF-IDF SCORES FOR KEYWORDS ON THE FUSE TELEVISION STATIONTerm Word Total callletters Word frequency occurrences occurrences tfldfFUSE Superstars 41 4 321505 3295426 FUSE Missy 23 5 321505 1478923 FUSEGaga 23 5 321505 1478923 FUSE Cent 23 6 321505 1232436 FUSE Jay-Z 23 8321505 924326.9 FUSE Elliott 23 11 321505 672237.7 FUSE Shakur 4 3321505 428673.4 FUSE Tupac 4 3 321505 428673.4

Step 8: Estimate Cost and CPM

An important part of the fitness of a particular media asset is its costand cost per thousand impressions (CPM). Ideally these will be low. TheCost and CPM estimates for upcoming media can be generated in severalways:

1. Quoted costs and CPM as provided by media publishers: For exampletelevision stations can list the cost and CPM rate for their differentprograms, rotations, and days of the week. These quoted costs may be (a)available electronically in which case they are collected, (b) availableafter inquiring with the publisher.

2. Estimated costs and CPM: Quoted costs and CPMs may not always befreely available for all media assets, and it may be too costly tocontact all publishers. As a result an estimate may be used to “fill in”costs and CPM for as many media assets as possible to identify whichones to potentially narrow in on and purchase. In order to create theseestimates, a variety of data providers maintain historical data on costsand CPMs for different media placements. In addition, the advertiser mayalso have purchased media before, and these costs and CPMs are alsoavailable. These can be used to forecast the future costs and CPMs. Amethod that can be used for forecasting cost and CPM is similar to thatdescribed in Step 7, which is to define the following summary tables:

StationPerformance(StationID,Cost,Revenue,Impressions,RevenuePerImpression,RevenuePerCost),StationDayPerformance(StationID,Day,Cost,Revenue,Impressions,RevenuePerImpression,RevenuePerCost),StationDayHourPerformance(StationID,Day,Hour,Cost,Revenue,Impressions,RevenuePerImpression,RevenuePerCost), andProgramPerformance(ProgramID,Cost,Revenue,Impressions,RevenuePerImpression,RevenueperCost).

Each time a particular media asset is used for advertising, the resultsare recorded into these tables. Historical data is also collected fromdata providers who monitor CPM and Costs of media. This is summarized tocreate average cost per impression and cost for each program, station,station-day, etc. For any new upcoming airing, a prediction ofperformance can be created based on the historical performance of the adin this same spot as:HistoricalCostPerImpressionPrediction(s)=c1*historcalStationCostPerImpression(s)+c2*StationProgramCostPerImpression(s)+c3*StationDayCostPerImpression(s)+c4*StationDayHourCostPerImpression(s)+c5

CPM(s)=1000* HistoricalCostPerImpressionPrediction(s)Cost(s)=HistoricalCostPerImpressionPrediction(s)*historicalImpressionPrediction(s)Step 9: Purchase Television Media Using Similarity

In order to purchase media, a media buyer would purchase media with ahigh fitness score, where fitness is defined below:

Fitness(p,s)=f(d(p,s), Cost(s), CPM(s),historicalImpressionPrediction(s), historicalRevenuePrediction (p,s),ContextualMatch(p,s), dv(p,s)),where p is the product, s is the media asset, d(p,s) is the demographicsimilarity between the product and media, Cost(s) is the cost of themedia, CPM(s) is the cost per thousand impressions of the media,historicalRevenuePRediction(p,s) is the predicted revenue of the productwere it to be advertised in the media s, ContextualMatch(p,s) is theword vector match between the product and media, and dv(p,s) is thedemographic similarity of a viewer panel.

A. Purchase Using Target Impressions and Target CPM

A specific embodiment of the current invention is using a metric definedas “target CPM” (tCPM) in order to produce an effective targeted mediabuy. This is a fitness function as defined above, but with some specificselections around the function form. This is defined as follows:

-   -   1. The similarity function dv(p,s) is equal to the correlation        coefficient between the product and media demographic vectors.    -   2. Fitness(p,s)=Cost(s)/(if(dv(p,s)<0 then 0.0 else dv(p,s))*        HistoricalImpressionPrediction(s))=tCPM

The quantity dv(p,s)* HistoricalImpressionPrediction(s) provides ameasure of the percentage of the sqrt(variance) in the product vectoraccounted for by this media asset (ignoring negative correlations). Thetool can now sort the media in order of tCPM since this corresponds tomedia with the best value per dollar.

B. Purchase Using Percent of Budget

Another specific embodiment of the current invention in terms ofpurchasing media, Is to define a “percent of budget” that should beideally allocated to a set of media assets. This is a fitness functionas defined above, but with some specific selections around the functionform. This is defined as follows:

Fitness(p,s)=tCPM(p,s)/SumS(tCPM(p,s))

C. Purchase Using Cluster Multi-Targeting:

One other specific embodiment to purchase media is what called “clustermulti-targeting”. If the overall product profile (the average) weretargeted, the result may be a targeted profile that either has very fewcustomers behind it, or which favors one duster over the others. Forexample, male, high-income programming might be favored, but there areactually dusters including females which won't be addressed.

In one embodiment to purchase media, all of the dusters can be targetedsimultaneously by aggregating the underlying segments together to forman overall tratio and timp based on the quality of the match for eachsegment. This can be done as follows.

Fitness(p,s)=cost(s)/timp(p,s)′,

where

Timp(p,s)′=Sum over c (timp(p,s)* membership % (c))

Imp(p,s)′=Sum over c (Imp(p,s)* membership % (c))

Tratio(p,s)′=timp(p,s)/imp(p,s).

In another embodiment, the system first sorts by duster, tcpm andassigns a rank-within-duster, so that the results for cluster 1 haverank 1 . . . R, and the results for cluster 2 also have ranks 1 . . . R.The system then sorts again by rank alone, so that the top results forduster 1, 2, and 3 are grouped together, and then the second best for 1,2, 3, and so on. The result is that the system simultaneously targetsall clusters.

D. Purchase Using Revenue Estimate

Another specific embodiment is to purchase media based on its estimatedrevenue prediction. This is a fitness function as defined above, butwhere the fitness function parameters were estimated to minimize thesquared error between predicted revenue and actual revenue produced bythe media.

min(Fitness(p,s)−Revenue(p,s))′

E. Purchase Using ROI Optimization

Another specific embodiment is for the system to select media topurchase based on the following constraints:

max Revenue subject to PredictedRevenue/Cost>minROI andPredictedCost<=Budget

The algorithm for solving the above optimization problem is as follows:

-   -   1. Budget=0; PredictedRevenue=0; s={ } (empty set);        BestSolution={ } (empty set)

Repeat steps 2 through 6 below until PredictedRevenue/PredictedCost>ROASor PredictedCost>Budget

-   -   2. Set Bestsolution=Bestsolution U {s}    -   3. Pick media s from the set of all available media S:max        PredictedRevenue(p,s)/PredictedCost(s)    -   4. PredictedRevenue=PredictedRevenue+Predicted Revenue(p,s)    -   5. PredictedCost=PredictedCost+PredictedCost(s)    -   6. Remove the selected media from the set of remaining media        S=S−{s} Return BestSolution

F. “Autopilot” Feature and Automated ROI Optimization

Another embodiment is to provide a list of recommended media. The usermay then accept/reject or edit the list of recommended media. Secondly,if the user has given the system the authority to do so, the system mayproceed and purchase the media on its own.

Reasons for Recommending Media Assets

Rather than just present a set of media assets to potentially purchasewith fitness score alone, the system can also provide the reason certainmedia assets are being suggested. When prompted by the user, the systemwill do the following: list the N components of the covariance matrixbetween p and s which have the highest magnitude, ie. show the set ofdemographic attributes

{i}: largest N from |p(i)*s(i)|

Intuitively these components exerted the greatest influence in drivingthe match score, and so are the “reasons” why the media was selected.Table XV shows an example of the program feature that provides reasonsfor explaining why certain programming was recommended. The product inthis case was the handyman tool product. The product was only matched ona small number of variables, but it shows the reason why each programwas selected—for example, the top match “FOX NASCAR SPRINT PRE” wasrecommended because this has a strong positive match with the product onthe demographic “perHH-P55-64” or persons aged 55-64. For a 400-variablematch, this feature would be even more useful. Using this feature, userscan uncover why a certain media asset was recommended, obtain moreconfidence in why the media is being recommended, and they can decide ifthey agree with the recommendation or would like to change it.

TABLE XV REASONS CERTAIN MEDIA ASSETS ARE RECOMMENDED FOR ADVERTISINGlookup key Program Corr why cov P-1 FOX NASCAR SPRINT PRE 0.8358perHH-P55-64 0.3385818 P-2 NASCAR SERIES AT DOVER 0.8357 perHH-P55-640.5546946 P-3 FOX NASCAR SPRINT PRE SAT 0.8241 perHH-P55-64 0.4557614P-4 FOX NASCAR SPRINT SAT PRE 0.8103 perHH-P55-64 0.4509219 P-5 FOXSPRINT CUP WNNRS CIRC 0.8036 perHH-MaleAdult 0.3706025 P-6 FOX NASCARSPRNT SAT WNNR 0.7996 perHH-P55-64 0.3920339 P-7 FOX NASCAR SPRNT WNNRSAT 0.7905 perHH-P55-64 0.4054008 P-8 MASH M-F 2 0.7901 perHH-P55-640.5861894 P-9 FOX SPRINT CUP PACE LAP 0.7791 perHH-MaleAdult 0.4735653P-10 FOX NASCAR SPRINT CUP PST 0.7787 perHH-P55-64 0.1984156

Table XVI below shows the final set of television airings that thesystem is recommending buying. This is based on scanning the upcomingtelevision guide (as described in the data feed setup), and assigning afitness score to each upcoming airing, and then presenting the programsin order of fitness:

TABLE XVI EXAMPLES OF TELEVISION AIRINGS RECOMMENDED BY THE SYSTEM FORPURCHASE Day of Hour of Fitness Station Start End Rotation Day of WeekHour of Day Score Program Station Score Date Date Rotation Score WeekScore Day Score 0.6419 Ritmo Aug. 14, 2011 Aug. 14, 2011 Sa + Su + Sun0.5575 3-6 PM (0.4558) Deportivo 4:30 PM 5:00 PM 9a + 8p 0.6419 RitmoAug. 21, 2011 Aug. 21, 2011 Sa + Su + Sun 0.5575 3-6 PM (0.4558)Deportivo 4:30 PM 5:00 PM 9a + 8p 0.6262 Descontrol Aug. 13, 2011 Aug.13, 2011 Sa + Su + Sat 0.3124 3-6 PM (0.4558) 5:00 PM 6:00 PM 9a + 8p0.6262 Descontrol Aug. 20, 2011 Aug. 20, 2011 Sa + Su + Sat 0.3124 3-6PM (0.4558) 5:00 PM 6:00 PM 9a + 8p 0.6239 Entourage Aug. 16, 2011 Aug.18, 2011 M + Su + Tue (0.2831) 3-6 AM 0.4971 4:30 AM 5:00 AM 12a + 6a0.6239 Entourage Aug. 13, 2011 Aug. 13, 2011 M + Su + Sat 0.3124 3-6 AM0.4971 4:30 AM 5:00 AM 12a + 6a 0.6239 Entourage Aug. 12, 2011 Aug. 12,2011 M + Su + Fri (0.6910) 3-6 AM 0.4971 4:30 AM 5:00 AM 12a + 6a 0.6239Entourage Aug. 17, 2011 Aug. 17, 2011 M + Su + Wed (0.4429) 3-6 AM0.4971 4:30 AM 5:00 AM 12a + 6a 0.6239 Entourage Aug. 18, 2011 Aug. 19,2011 M + Su + Fri (0.6910) 3-6 AM 0.4971 4:30 AM 5:00 AM 12a + 6a 0.6239Entourage Aug. 25, 2011 Aug. 25, 2011 M + Su + Thu (0.4277) 3-6 AM0.4971 4:30 AM 5:00 AM 12a + 6a

Example of Media Buying Using Product-Spot Similarity

FIG. 1.12 and FIG. 1.13 show the number of phone orders received peradvertisement airing and its dependence upon similarity scores. Bothplots indicate an increase in revenue per airing as demographicsimilarity increases (more negative scores). As a result, as televisionmedia becomes more similar to the target product, revenue per airingincreases. Table XVII shows similarity versus revenue per airing. Thetable is sorted by the similarity score from most similar (most negativescore) to least similar (most positive score). This table can be used todecide if there is a similarity threshold for which an advertisement isplaced. The variable d is the similarity score, mean(orders) is the meannumber of order placed, and n is the number of stations on which anadvertisement is run to produce the results in this table. For asimilarity of around −0.06, the lower similarity stations out-performthe others with around 4.2× better performance in terms of orders perspot. This difference is also statistically significantly as measuredusing a Wilcoxon rank sum test (this test looks at the comparative ranksof station performance and notes the probability that ranks from onegroup would appear consistently ahead of the other). A large amount oftelevision station inventory is available at this threshold. In themedia campaign used to generate Table XVII, 60% of the budget spent onadvertising was spent on stations this good or better.

A company looking for maximum performance could achieve even betterresults however. It could restrict its media campaign to only the mostsimilar television stations to the product. If this strategy is pursued,in one scenario, similarities <−0.20 can be used. In this case a 7.3×performance gain might be achieved in terms of orders per spot. At thissignificantly higher performance, 36% of the media budget can be spenton this higher performing inventory. This is still a very high amount ofspend, given that the television assets are so much higher performing.

An example of media buying would be to

1. Determine the desired Media Efficiency Ratio (revenue/cost ratio)

2. Look up Table XVII for the desired MER, and read-off the similaritythreshold.

3. Buy all media assets that have this similarity or better.

TABLE XVII SIMILARITY THRESHOLD VS ADVERTISING PERFORMANCE p-value RatioCost | mean(orders) | mean(orders) | orders n | of similarity < d dsimilarity < d similarity > d (Wilcoxon) similarity < d means (prop oftotal) −0.2646 2.475 0.2581 0.0986 2 9.59 0.24 −0.2469 1.8167 0.24880.0534 3 7.30 0.37 −0.1764 1.6958 0.2054 0.0105 4 8.26 0.44 −0.16511.3567 0.2139 0.0678 5 6.34 0.45 −0.1313 1.1722 0.2124 0.0785 6 5.520.53 −0.1267 1.0966 0.1928 0.0246 7 5.69 0.53 −0.1035 1.022 0.17820.0114 8 5.74 0.53 −0.0988 0.9202 0.1818 0.0189 9 5.06 0.59 −0.06820.8281 0.1814 0.0601 10 4.33 0.60 −0.0153 0.7529 0.202 0.1484 11 3.730.65 0.0069 0.7214 0.1918 0.1079 12 3.76 0.67 0.0097 0.6902 0.18410.0839 13 3.75 0.69 0.0099 0.6409 0.1984 0.1817 14 3.26 0.75 0.01920.6297 0.1766 0.108 15 3.57 0.76 0.0208 0.6008 0.1773 0.1063 16 3.390.79 0.0219 0.5654 0.1921 0.2149 17 2.94 0.82 0.0238 0.542 0.1966 0.225418 2.76 0.82 0.0265 0.5134 0.2162 0.4049 19 2.37 0.84 0.0298 0.48780.2403 0.6599 20 2.03 0.84 0.034 0.4645 0.2703 0.9798 21 1.72 0.840.0389 0.4434 0.3089 0.7113 22 1.44 0.84 0.0432 0.4467 0.274 0.9555 231.63 0.87 0.0639 0.4409 0.2672 0.8574 24 1.65 0.87 0.065 0.4233 0.3340.7678 25 1.27 0.90 0.0756 0.407 0.4454 0.3342 26 0.91 0.90 0.08330.3919 0.6681 0.0549 27 0.59 0.95

Because there are so many different variables that can be used to builda fitness model, a model management infrastructure can be useful. Themanagement infrastructure assigns a unique model identification code toeach fitness model. The estimates for a particular fitness model can betested against other models to determine which model performs better.

Fitness predictions can be recorded in a schema asFitnessPredictionProduction (ProjectKey, MediaInstanceID, Date, Time,MediaAssetID, Score) tuples. This representation is used to avoid movingcode during a new fitness model release.

A second area—a modeling schema—is maintained where predictions aregenerated, and simultaneously flight multiple fitness models. The codein this area may not meet the same standard as for the productionsystem. The code is available to analysts and can be modified in orderto develop new models.

In this schema results are written as FitnessPrediction (ProjectKey,MediaInstanceID, Date, Time, MediaAssetID, ModelID, Score). Anothertable, FitnessModel (modelid, Description) keeps track of the fitnessmodel that has been enabled for each project.

The production system performs two steps. First, it runs a defaultfitness model in production which populates a set of Fitnesspredictions. This code is designed to be reliable and is changed on aslower time-scale. Next, it then joins to the underlyingFitnessPrediction table to retrieve model results. If model results areavailable in the proper format it will retrieve these results and usethem in production. Every day FitnessPrediction is archived in aFitnessPredictionHistory table to ensure that model results can betracked over time.

In the modeling schema area, it is possible to run multiple models inparallel, record their predictions, preview different models prior torelease, and so on.

As a result of this architecture, releasing a new fitness model can beachieved without moving any code into the production system—keeping itsafe and appropriately isolated while still allowing rapid iteration onmodels through a controlled interface. Eventually the new models shouldbe migrated to become the new default model in production, howeveroperationally new models can be deployed and used in production forextended periods of time, and then productized when needed. Thisincreases reliability and model release speed and simultaneouslysupports prototyping and model development which occurs in parallel withthe production model.

Examples of Results Provided by an Automated Program Finder

An automated program finder can be used to implement the techniquesdescribed above.

FIG. 2 shows an example screenshot of different segments that can betargeted using the program finder. The program finder is using data thatcorresponds to a product or project code-named Jawhorse. Naturalclusters 1-3 are clusters generated unsupervised clustering. Othertargeted segments have also been passed into the program finder by theclient, such as infomercial purchase type, retail purchase type, andretail website purchase type. Any of these segments are targetable.

FIG. 3 shows an example result produced by the program finder forrotation media assets. The program finder was set to use a “rotation”media asset pattern where the station designates a window of time over anumber of designated days that includes more than one program, and thestation decides when an advertisement will run within that time window.Additionally, the program finder used an overall segment, rather thanparticular client-specified segments or natural clustering. The resultincludes 14 ranked media asset patterns.

FIG. 4 shows an example recommended allocation of an advertising budgetto particular program stations for the product “Jawhorse”. Theallocation is based on ratecard cost and assumes a linear value functionbased on correlation coefficient. Ratecard cost is media cost asmonitored by a third-party rating agency, such as SQAD, or an initialstarting price cost that is defined by the publisher that is selling themedia for advertising, per-negotiation.

FIG. 5 shows an example of graph of targeted impressions and impressionsas an increasing number of media assets are added to the advertisingpurchase. As less-well-targeted programming is added, the ratio betweentargeted and untargeted impressions decreases.

FIG. 6 shows an example screenshot of a program finder result. Theresult includes a graph of targeted impressions and impressions for agiven budget level. The system automatically calculates the inflectionpoint in the budget where the best ratio of targeted impressions toimpressions is achieved, above which the targeted impressions havediminishing returns.

The result also includes a sortable list of suggested programs. The listis sortable in order of targeting ratio (tratio) which is the mostrelevant programming or the programming with the closest vector match.The list is also sortable in order of target cost per thousandimpressions (tCPM) which is the media with the best target impressionsper dollar spent. Media buyers typically buy based upon tCPM. The listcan also be sorted by revenue estimate (not shown).

FIG. 7 shows an example screenshot showing different media assetpatterns that can be compared to the product. The match scores for eachof these media asset patterns are combined to form a final score. Thematch scores in FIG. 7 are in columns to the right which are off-screen.

FIG. 8 shows an example screenshot of how customer records are input tothe program finder. The user can select whether to upload customer namesand addresses or whether to manually type in the profile to be targeted.If the user uploads a customer name-address file, it is automaticallyenriched to obtain demographic information, and then a product vector isgenerated for matching to media.

FIG. 9 shows an example screenshot of a program finder profile builder.The profile customer targeted in this example is a high incomerepublican female in her 20's. Other variables can also be selected froma list of 400 variables, such as car ownership, interest in sciencefiction, etc. After entering the desired customer profile, allprogramming that matches this type of customer can be identified basedon linked buyers or viewer records. While the program finder permits auser to manually enter a customer profile for targeting, the typicalmethod is for the program finder to automatically ingest customeraddress records, enrich the records with demographics, and thensummarize the records into a demographic vector that is based on actualbuyers.

FIG. 10 shows an example result of top media asset rotations determinedby the program finder for a targeted customer having the followingcharacteristics: high income, republican, female, in her 20's.

FIG. 11 shows an example program finder result corresponding to programmedia asset patterns.

CONCLUSION

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense; that is to say, in the sense of“including, but not limited to.” As used herein, the terms “connected,”“coupled,” or any variant thereof means any connection or coupling,either direct or indirect, between two or more elements; the coupling orconnection between the elements can be physical, logical, or acombination thereof. Additionally, the words “herein,” “above,” “below,”and words of similar import, when used in this application, refer tothis application as a whole and not to any particular portions of thisapplication. Where the context permits, words in the above DetailedDescription using the singular or plural number may also include theplural or singular number respectively. The word “or,” in reference to alist of two or more items, covers all of the following interpretationsof the word: any of the items in the list, all of the items in the list,and any combination of the items in the list.

The above Detailed Description of examples of the invention is notintended to be exhaustive or to limit the invention to the precise formdisclosed above. While specific examples for the invention are describedabove for illustrative purposes, various equivalent modifications arepossible within the scope of the invention, as those skilled in therelevant art will recognize. For example, while processes or blocks arepresented in a given order, alternative implementations may performroutines having steps, or employ systems having blocks, in a differentorder, and some processes or blocks may be deleted, moved, added,subdivided, combined, and/or modified to provide alternative orsubcombinations. Each of these processes or blocks may be implemented ina variety of different ways. Also, while processes or blocks are attimes shown as being performed in series, these processes or blocks mayinstead be performed or implemented in parallel, or may be performed atdifferent times. Further any specific numbers noted herein are onlyexamples: alternative implementations may employ differing values orranges.

The teachings of the invention provided herein can be applied to othersystems, not necessarily the system described above. The elements andacts of the various examples described above can be combined to providefurther implementations of the invention. Some alternativeimplementations of the invention may include not only additionalelements to those implementations noted above, but also may includefewer elements.

Any patents and applications and other references noted above, includingany that may be listed in accompanying filing papers, are incorporatedherein by reference. Aspects of the invention can be modified, ifnecessary, to employ the systems, functions, and concepts of the variousreferences described above to provide yet further implementations of theinvention.

These and other changes can be made to the invention in light of theabove Detailed Description. While the above description describescertain examples of the invention, and describes the best modecontemplated, no matter how detailed the above appears in text, theinvention can be practiced in many ways. Details of the system may varyconsiderably in its specific implementation, while still beingencompassed by the invention disclosed herein. As noted above,particular terminology used when describing certain features or aspectsof the invention should not be taken to imply that the terminology isbeing redefined herein to be restricted to any specific characteristics,features, or aspects of the invention with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the invention to the specific examplesdisclosed in the specification, unless the above Detailed Descriptionsection explicitly defines such terms. Accordingly, the actual scope ofthe invention encompasses not only the disclosed examples, but also allequivalent ways of practicing or implementing the invention.

What is claimed is:
 1. A computer-implemented method of profilingcustomers of a product, the method comprising: identifying previouscustomers of the product using information from previous product orders;creating enriched customer records for the previous customers usingdemographic information, wherein the demographic information includesmultiple variables; creating a product profile from the enrichedcustomer records; and using the product profile to select advertisingmedia for the product.
 2. The method of claim 1, wherein using theproduct profile comprises: calculating a standardized score for at leastsome of the variables for the product; using the standardized scores toidentify variables characterizing the previous customers; and designingan advertisement for the product based on the identified variables. 3.The method of claim 2, wherein the standardized score is a z-score. 4.The method of claim 1, further comprising using clustering to identifymultiple segments of customers.
 5. A computer-implemented method ofprofiling a media asset, the method comprising: identifying knowncustomers of one or more products advertised with the media asset;creating enriched customer records for the known customers usingdemographic information, wherein the demographic information includesmultiple variables; creating a media asset profile from the enrichedcustomer records; and using the media asset profile to determineproducts to advertise with the media asset.
 6. The method of claim 5,wherein using the media asset profile comprises: calculating astandardized score for at least some of the variables for the mediaasset; using the standardized scores to identify variablescharacterizing the media asset; and determining product categories toadvertise with the media asset based on the identified variables.
 7. Themethod claim 5, wherein identifying known customers comprises usinglinking keys in an advertisement with the media asset.
 8. The method ofclaim 6, wherein the standardized score is a z-score.
 9. The method ofclaim 5, wherein multiple products are advertised with the media asset,the method further comprising: creating a media asset profile and acentroid from a reference population for each of the multiple productsfrom the enriched customer records; subtracting the media asset profilecentroid from the media asset profile for each of the multiple productsto obtain an unbiased media asset profile.
 10. The method of claim 5,further comprising at least one of: reporting media asset demographicsas an index showing a proportion of demographics on each media asset,reporting media asset demographics as population equivalent numbers ofviewers on each media asset, and predicting demographic viewership ofthe media asset.
 11. A computer-implemented method of profiling a mediaasset, the method comprising: using demographic information for a knownaudience that views the media asset, wherein the demographic informationincludes multiple variables; creating a media asset profile from thedemographic information; calculating a standardized score for at leastsome of the variables for the media asset; using the standardized scoresfor the at least some of the variables to determine product categoriesto advertise with the media asset.
 12. The method of claim 11, whereinthe audience is a panel of television viewers, and the demographicinformation is provided by the panel.
 13. The method of claim 11,wherein members of the audience have unique set-top boxes for recordingtelevision viewing history, and the demographic information is providedby the members of the audience. 14.-21. (canceled)